TR: Recursive Auto-Associative Memory

[pollack@nmsu.csnet]

             Recursive Auto-Associative Memory:
     Devising Compositional Distributed Representations

                       Jordan Pollack

                        MCCS-88-124

               Computing Research Laboratory
                New Mexico State University
                    Las Cruces, NM 88003

     A major outstanding problem for connectionist models is
the  representation  of variable-sized recursive and sequen-
tial data structures, such as trees and  stacks,  in  fixed-
resource  systems.   Some  design  work  has  been  done  on
general-purpose distributed representations with some  capa-
city  for  sequential or recursive structures, but no system
to date has developed its own.

     This paper presents connectionist mechanisms along with
a  general  strategy  for  developing  such  representations
automatically: Recursive Auto-associative Memory  (RAAM).  A
modified  autoassociative error-propagation learning regimen
is used to develop fixed-width  representations  and  access
mechanisms  for  stacks and trees. The strategy involves the
co-evolution of the  training  environment  along  with  the
access  mechanisms  and  distributed representations.  These
representations  are  compositional,  similarity-based,  and
recursive,  and  may lead to many new applications of neural
networks to traditionally symbolic tasks.  Several  examples
of its use are given.

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